Token Classification
Transformers
PyTorch
Catalan
roberta
catalan
named entity recognition
ner
CaText
Catalan Textual Corpus
Eval Results (legacy)
Instructions to use projecte-aina/roberta-base-ca-v2-cased-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use projecte-aina/roberta-base-ca-v2-cased-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="projecte-aina/roberta-base-ca-v2-cased-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("projecte-aina/roberta-base-ca-v2-cased-ner") model = AutoModelForTokenClassification.from_pretrained("projecte-aina/roberta-base-ca-v2-cased-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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ner_results = nlp(example)
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pprint(ner_results)
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```
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## Limitations and bias
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ner_results = nlp(example)
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pprint(ner_results)
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[{'entity_group': 'person-other', 'score': 0.90749526, 'word': ' George Smith Patton', 'start': 0, 'end': 19}, {'entity_group': 'organization-other', 'score': 0.9260813, 'word': ' Ejército de los Estados Unidos', 'start': 39, 'end': 69}, {'entity_group': 'location-other', 'score': 0.992481, 'word': ' Europa', 'start': 73, 'end': 79}, {'entity_group': 'event-attack/terrorism/militaryconflict', 'score': 0.99319077, 'word': ' Segunda Guerra Mundial', 'start': 91, 'end': 113}]
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```
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## Limitations and bias
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